Automatic facial recognition has a wide range of applications in the commercial, military, and government sectors, spanning from tagging people in social networking websites to surveillance for homeland security. Face recognition research has predominantly focused on the visible spectrum, addressing challenges such as illumination variations, pose, and image resolution. However, for surveillance during nighttime, the lack, or absence, of illumination prevents cameras operating in the visible-light spectrum from being used discreetly and effectively. Thermal imaging measures radiation in the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectra, which is naturally emitted by living tissue, and therefore is a highly practical imaging modality for nighttime operation. However, as most databases and watch lists only contain facial imagery in the visible spectrum, it is difficult to match an unknown thermal probe image of an individual's face to a set of known visible gallery images. This is referred to as cross-modal or heterogeneous face recognition: seeking to match probe face images acquired in one imaging modality to gallery face images from a different imaging modality.
Several recent efforts have attempted to address cross-modal, thermal-to-visible face recognition. Due to the large modality gap caused by differences in phenomenology (reflectance for visible imaging and emittance for thermal imaging), the measured visible face signatures are very different from the thermal face signatures. Recently, methods consisting of preprocessing, feature extraction, and classification have been met with limited success however, since identification performance was still less than 55% for thermal-to-visible face recognition. Thermal-to-visible face recognition algorithm performance may be fundamentally limited by the degree of correlation between the visible and thermal facial signatures, due to phenomenology and the lower spatial resolution in the thermal spectrum arising from the longer wavelength.
Therefore, there is a need in the art for improved cross-modal face matching.